Statistical wind direction modeling for the analysis of large scale wind power generation

Understanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley & Sons, Ltd.     

Effect of Climate Change on wind speed and its impact on optimal power system expansion planning: The case of Chile

This work assesses the changes in power capacity expansion decisions regarding power generation and transmission that occur when the effects of Climate Change on wind speed are captured in the decision model. Considering an 85-year period (2016–2101), we use a Mixed-Integer Linear Program (MILP) model to analyze the optimal power capacity expansion in diverse types of power generation technologies, throughout the years and geographical locations. The optimization model minimizes the total (investment and operational) costs of the power system subject to several technical and economic constraints. We implement our model using the main Chilean power system. We compare two scenarios: one assuming that Climate Change affects wind speeds and hence wind farm capacity factors and the other assuming it does not. Our results reveal that, when taking into account the impact of Climate Change on wind speed, the optimal power generation and transmission expansion plan is different than when ignoring this effect. The variation of wind speed affects not only wind power capacity installed, but also other-technology power capacity installed. In particular, power capacity installed in wind and solar generation plants is higher (measured in MW installed) than the power capacity installed when we ignore the effects of Climate Change; and power capacity installed in diesel and natural gas technologies are lower. We perform sensitivity analyses, changing power capacity expansion limits and the discount rate, to check for the robustness of our results.     

Revision of reserve requirements following wind power integration in island power systems

This contribution studies the impact of wind power on the operation of island power systems. The analysis focuses on the available flexibility of thermal generation to balance wind power variations and prediction errors. This issue is highly relevant for small and medium sized islands where interconnections are absent and the smoothing of variations is limited due to a small geographical surface. The main objective is to determine if additional reserve requirements are necessary for ensuring reliable wind power integration in an isolated transmission system. In this context, a case study is performed for Cyprus, a medium-sized island, where wind developments towards 2020 schedule an installed capacity of 300 MW, reaching 7% of the annual electricity consumption. Simulations with installed wind power capacities up to 400 MW show that the current available flexibility in the generation system is inadequate to balance real-time wind power fluctuations and prediction errors. Consequently, large amounts of wind curtailment and demand shedding may be expected. Therefore, current reserve requirements should be revised, in order to reliably facilitate wind power into the system. Furthermore, the impact of introducing natural gas for electricity generation in Cyprus on the reserve requirements, following wind power integration, is examined.     

Impact of hydrogen energy storage on California electric power system: Towards 100% renewable electricity

Decarbonization of the power sector is a key step towards greenhouse gas emissions reduction. Due to the intermittent nature of major renewable sources like wind and solar, storage technologies will be critical in the future power grid to accommodate fluctuating generation. The storage systems will need to decouple supply and demand by shifting electrical energy on many different time scales (hourly, daily, and seasonally). Power-to-Gas can contribute on all of these time scales by producing hydrogen via electrolysis during times of excess electrical generation, and generating power with high-efficiency systems like fuel cells when wind and solar are not sufficiently available. Despite lower immediate round-trip efficiency compared to most battery storage systems, the combination of devices used in Power-to-Gas allows independent scaling of power and energy capacities to enable massive and long duration storage. This study develops and applies a model to simulate the power system balance at very high penetration of renewables. Novelty of the study is the assessment of hydrogen as the primary storage means for balancing energy supply and demand on a large scale: the California power system is analyzed to estimate the needs for electrolyzer and fuel cell systems in 100% renewable scenarios driven by large additions of wind and solar capacities. Results show that the transition requires a massive increase in both generation and storage installations, e.g., a combination of 94 GW of solar PV, 40 GW of wind, and 77 GW of electrolysis systems. A mix of generation technologies appears to reduce the total required capacities with respect to wind-dominated or solar-dominated cases. Hydrogen storage capacity needs are also evaluated and possible alternatives are discussed, including a comparison with battery storage systems.     

Simulating subhourly variability of wind power output

As installed wind power capacity grows, subhourly variability in wind power output becomes increasingly important for determining the system flexibility needs, operating reserve requirements, and cost associated with wind integration. This paper presents a new methodology for simulating subhourly wind power output based on hourly average time series, which are often produced for system planning analyses, for both existing wind plants and expanded, hypothetical portfolios of wind plants. The subhourly model has an AR(p)‐ARCH(q) structure with exogenous input in the heteroskedasticity term. Model coefficients may be fit directly to high‐pass filtered historical data if it exists; for sets of wind plants containing hypothetical plants for which there are no historical data, this paper presents a method to determine model coefficients based on wind plant capacities, capacity factors, and pairwise distances. Unlike predecessors, the model presented in this paper is independent of wind speed data, captures explicitly the high variability associated with intermediate levels of power output, and captures distance‐dependent correlation between the power output of wind plants across subhourly frequencies. The model is parameterized with 1‐minute 2014 plant‐level wind power data from Electric Reliability Council of Texas (ERCOT) and validated out‐of‐sample against analogous 2015 data. The expanded‐capacity model, fit to 2014 data, produces accurate subhourly time series for the 2015 wind fleet (a 49% capacity expansion) based only on the 2015 system's wind plant capacities, capacity factors, and pairwise distances. This supports its use in simulating subhourly fleet aggregate wind power variability for future high‐wind scenarios.     

Characteristics of day‐ahead wind power forecast errors in Nordic countries and benefits of aggregation

The growing proportion of wind power in the Nordic power system increases day‐ahead forecasting errors, which have a link to the rising need for balancing power. However, having a large interconnected synchronous power system has its benefits, because it enables to aggregate imbalances from large geographical areas. In this paper, day‐ahead forecast errors from four Nordic countries and the impacts of wind power plant dispersion on forecast errors in areas of different sizes are studied. The forecast accuracy in different regions depends on the amount of the total wind power capacity in the region, how dispersed the capacity is and the forecast model applied. Further, there is a saturation effect involved, after which the reduction in the relative forecast error is not very large anymore. The correlations of day‐ahead forecast errors between areas decline rapidly when the distance increases. All error statistics show a strong decreasing trend up to the area sizes of 50,000 km². The average mean absolute error (MAE) in different regions is 5.7% of installed capacity. However, MAE of a smaller area can be over 8% of the capacity, but when all the Nordic regions are aggregated together, the capacity‐normalized MAE decreases to 2.5%. The average of the largest errors for different regions is 39.8% and when looking at the largest forecast errors for smaller areas, the largest errors can exceed 80% of the installed capacity, whereas at the Nordic level, the maximum forecast error is only 13.5% of the installed capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

Cost‐optimized allocation of wind power investments: a Nordic–German perspective

Using a linear cost minimization model with a 1 h time resolution, we investigated the influence of geographic allocation of wind power on large‐scale wind power investments, taking into account wind conditions, distance to load, and the nature of the power system in place (i.e. power generation and transmission capacities). We employed a hypothetical case in which a 20% wind power share of total electricity demand is applied to the Nordic–German power system. Free, i.e. geographically unrestricted, allocation of new wind power capacity is compared with a case in which national planning frameworks impose national limitations on wind power penetration levels. Given the cost assumptions made in the present work, the prospect of increasing the wind power capacity factor from 20 to 30% could motivate investments in transmission capacity from northern Scandinavia to continental Europe. The results obtained using the model show that the distribution of wind farms between regions with favorable wind conditions is dependent upon two factors: (i) the extent to which existing lines can be used to transmit the electricity that results from the new wind power and (ii) the correlation for wind power generation between the exporting region and the wind power generation already in place. In addition, the results indicate that there is little difference, i.e. just over 1%, in total yearly cost between the free allocation of new wind power and an allocation that complies with national planning frameworks. However, on a national level, there are significant differences with respect to investments in transmission and wind power capacities and the replacement of conventional power generation. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of different weather years on the design of hydrogen supply pathways for transport needs

Renewable energy sources (RES) will play a crucial role in future sustainable energy systems. In scenarios analyzing future energy system designs, a detailed spatial and temporal representation of renewable-based electricity generation is essential. For this, sufficiently representative weather data are required. Most analyses performed in this context use the historical data of either one specific reference year or an aggregation of multiple years. In contrast, this study analyzes the impact of different weather years based on historical weather data from 1980 through 2016 in accordance with the design of an exemplary future energy system. This exemplary energy system consists of on- and offshore wind energy for power-to-hydrogen via electrolysis, including hydrogen pipeline transport for most southwestern European countries. The assumed hydrogen demand for transportation needs represents a hypothetical future market penetration for fuel cell-electric vehicles of 75%. An optimization framework is used in order to evaluate the resulting system design with the objective function of minimizing the total annual cost (TAC) of the system. For each historical weather year, the applied optimization model determines the required capacities and operation of wind power plants, electrolyzers, storage technologies and hydrogen pipelines to meet the assumed future hydrogen demand in a highly spatially- and temporally-detailed manner, as well as the TAC of the system. Following that, the results of every individual year are compared in terms of installed capacities, overall electricity generation and connection to the transmission network, as well as the cost of these components within each region. The results reveal how sensitive the final design of the exemplary system is to the choice of the weather year. For example, the TAC of the system changes by up to 20% across two consecutive weather years. Furthermore, significant variation in the optimization results regarding installed capacities per region with respect to the choice of weather years can be observed.     

Simulation of aggregate wind farm short-term production variations

The variability of wind power production poses the greatest challenge in the integration of large-scale wind power in power systems. Furthermore, larger-scale penetration implies a wider geographical spreading of installed wind power, resulting in reduced variability and the smoothing effect of total power generation. Therefore, analysis of the impact of wind power variations on power system operation requires adequate modeling of aggregate power output from geographically dispersed wind farms. This paper analyzes different aspects of Markov chain Monte Carlo simulation methods for the synthetic generation of dependent wind power time series. However, testing indicates that these approaches do not adequately model the stochastic dependence between wind power time series in conjunction with individual persistence which is necessary to obtain realistic distributions of aggregate power output and total power variations. Consequently, a novel approach based on a modified second-order Markov chain Monte Carlo simulation is proposed. Simulation results show that this method obtains synthetic time series of aggregate wind power which very closely fit the original data, with respect to both the cumulative density function of total output power and the probability density function of power variations.     

风光互补电动汽车储能电站系统优化设计

通过对唐山市区太阳能和风能资源状况调查分析,对全年不同方位角和倾角上的太阳能辐射量进行模拟计算,得出南偏东9.8°方向、倾角为39.7°的倾斜面上接收的太阳能辐射量最大,其值为1.62×106Wh/m2。研究中对3kW风力发电机和1kW光伏发电系统的发电量进行了计算,并以1辆纯电动轿车为负载进行了容量配比优化,设计了风力发电系统、风光互补系统及光伏系统三种不同的方案,经过对各方案的经济性、可靠性及稳定性分析,得出最佳的设计方案为风光互补发电系统,该系统风力发电装机容量为3kW,光伏发电装机容量为8.96kW。     

Forecast study of the supply curve of solar and wind technologies in Argentina,Brazil, Chile and Mexico

This paper forecasts the supply curve of non-conventional renewable technologies such as wind and solar generating stations in Argentina, Brazil, Chile and Mexico using technological and economic parameters. It also estimates the additional investment costs in solar and wind generation for reaching the renewable energy target in each of these countries. To assess the power supply profile from 1 axis tracking PV and horizontal axis wind turbine (three blade) stations, two different scenarios are developed for 2014 and 2025. Scenario 1 estimates the PV and wind annual electricity yield by using polycrystalline silicon (cSi poly) as semiconductor material for PV cells and a Vestas 90–3.0 MW turbine for the wind for 2014.Scenario 2 assumes a more efficient technology, such as CPV. In fact, the model employs 45% efficiency triple junction cells using ∼3500 m2 for each 1 MW installed capacity in 2025. Moreover, this scenario also assumes a more powerful type of turbine, i.e. Vestas 112–3.075 MW. The biggest potential for wind power is found to be in Argentina, followed by Brazil, Mexico and Chile. In addition, a 550 MW installed capacity CPV power station, using triple junction cells could generate up to 4 TWh in Chile in 2025.     

Does bulk electricity storage assist wind and solar in replacing dispatchable power production?

This paper discusses the impact of bulk electric storage on the production from dispatchable power plants for rising variable renewable electricity shares. Two complementary optimization frameworks are used to represent power systems with a varying degree of complexity. The corresponding models approximate the wholesale electricity market, combined with the rational retirement of dispatchable capacity. Two different generic storage technologies are introduced exogenously to assess their impact on the system.The analysis covers two countries: France, where the power supply's large nuclear share allows for the discussion of storage impact on a single generator type; and Germany, whose diverse power supply structure enables storage interactions with multiple electricity generators. In the most general case, additional storage capacity increases dispatchable power production (e.g. nuclear, coal) for small wind and solar shares, i.e. it compensates the replacement induced by renewable energies. For larger variable renewable electricity volumes, it actively contributes to dispatchable power replacement. In a diverse power system, this results in storage-induced sequential mutual replacements of power generation from different plant types, as wind and solar capacities are increased.This mechanism is strongly dependent on the technical parameters of the storage assets. As a result, the impact of different storage types can have opposite signs under certain circumstances. The influence of CO₂ emission prices, wind and solar profile shapes, and power plant ramping costs is discussed.     

A variance analysis of the capacity displaced by wind energy in Europe

Wind energy generation distributed all over Europe is less variable than generation from a single region. To analyse the benefits of distributed generation, the whole electrical generation system of Europe has been modelled including varying penetrations of wind power. The model is chronologically simulating the scheduling of the European power plants to cover the demand at every hour of the year. The wind power generation was modelled using wind speed measurements from 60 meteorological stations for 1 year. The distributed wind power also displaces fossil‐fuelled capacity. However, every assessment of the displaced capacity (or a capacity credit) by means of a chronological model is highly sensitive to single events. Therefore the wind time series was shifted by integer days against the load time series, and the different results were aggregated. The same set of results is shown for two other options, one where the pump storage plants are used more aggressively and the other where all German nuclear plants are shut off. NCEP/NCAR reanalysis data have been used to recreate the same averaged time series from a data set spanning 34 years. Through this it is possible to set the year studied in detail into a longer‐term context. The results are that wind energy can contribute more than 20% of the European demand without significant changes in the system and can replace conventional capacity worth about 10% of the installed wind power capacity. The long‐term reference shows that the analysed year is the worst case for wind power integration. Copyright © 2006 John Wiley &Sons, Ltd.     

Flexible power generation scenarios for biogas plants operated in Germany: impacts on economic viability and GHG emissions

Biogas plants enable power to be generated in a flexible way so that variable, renewable energy sources can be integrated into the energy system. In Germany, the Renewable Energy Sources Act promotes flexible power generation in biogas plants. Two existing biogas plants in flexible operation were analyzed with respect to economic viability and greenhouse gas (GHG) emissions to assess the feasibility of flexible operation. To do this, a biogas technology simulation model was developed to reproduce the technical design of both biogas plants and to link this design with twelve flexibilization scenarios. The evaluation of the economic viability is based on a discounting method of investment appraisal. For assessing the level of GHG emissions, the life cycle assessment method has been applied. The results show that the profitability of flexibilization is contingent upon premium payments promoting flexibility and direct sales resulting from a higher electrical efficiency of new or additionally installed combined heat and power units. Overall, with respect to profitability, the results of the flexible power generation scenarios are dependent upon the properties of the technical plant, such as its power generation and gas storage capacities. Relative GHG emissions from flexible biogas plants show significantly lower values than for referenced fossil gas–steam power stations. Among the various scenarios, the results reveal that the level of GHG emissions especially depends on the number of operating hours of the additional combined heat and power unit(s). The results of the analyzed biogas plants showed no direct correlation between GHG emissions and the economic benefits. Overall, a flexible power generation of biogas plants may improve the economic viability as well as result in lower GHG emissions in comparison with a conventional base load operation. © 2016 The Authors. International Journal of Energy Research published by John Wiley & Sons Ltd.     

Analysis of impacts of wind integration in the Tamil Nadu grid

As the share of wind in power systems increases, it is important to assess the impact on the grid. This paper combines analysis of load and generation characteristics, generation adequacy and base and peak load variations to assess the future role of wind generation. A simulation of Tamil Nadu in India, with a high penetration of wind power (27% by installed capacity), shows a capacity credit of 22% of the installed wind capacity. For seasonal wind regimes like India, neither the capacity factor, nor the capacity credit reflects the monthly variation in the wind generation. A new approach based on the annual load duration curve has been proposed for generation expansion planning with higher penetration of wind. The potential savings in base and peak capacity required with increasing wind power have been quantified. A future scenario for Tamil Nadu for 2021 has been illustrated. It was found that 5500 MW of wind power can save 3200 MU of peak energy required or an average peak capacity of 2400 and 1100 MW of base capacity. This analysis would be useful to assess the future impacts of increasing wind capacity in grids.     

Impact of hourly wind power variations on the system operation in the Nordic countries

The variations of wind power production will increase the flexibility needed in the system when significant amounts of load are covered by wind power. When studying the incremental effects that varying wind power production imposes on the power system, it is important to study the system as a whole: only the net imbalances have to be balanced by the system. Large geographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output. The goal of this work was to estimate the increase in hourly load‐following reserve requirements based on real wind power production and synchronous hourly load data in the four Nordic countries. The result is an increasing effect on reserve requirements with increasing wind power penetration. At a 10% penetration level (wind power production of gross demand) this is estimated as 1·5%–4% of installed wind capacity, taking into account that load variations are more predictable than wind power variations. Copyright © 2004 John Wiley & Sons, Ltd.     

Model-based analysis of effects from large-scale wind power production

For an economically and ecologically optimised integration of fluctuating renewable power generation (especially wind power) into electricity generation, a detailed consideration of fluctuation-induced effects on the existing power system is essential. A model-based approach is introduced in this paper, which comprehensively analyses the impact of such effects on power plant scheduling and facilitates their integration into the development of strategies for an optimised evolution of the future power system structure. The newly developed Aeolius tool for the simulation of power plant scheduling is described. In a combined analysis of long- and short-term effects it is used together with the multi-periodic cost-optimising energy system model PERSEUS-CERT. Based on the Matlab/Simulink^® package, Aeolius considers the challenges for plant scheduling down to a time scale of 10 min. Special attention is paid to the provision of stand-by capacities and control power, as well as intermediate storage. Thus, a sophisticated quantification of the actual (net) benefits of wind power feed-in is achieved. Model results for Germany show that wind mainly substitutes power from intermediate-load and base-load plants (coal-, lignite-, and nuclear-fired). However, the required provision of stand-by capacities and control power does not only limit the substitution of conventional capacities, but also the achievable net savings of fuel and emissions in conventional power generation.     

Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

Short‐term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per‐horizon‐basis, meaning that they lack information about the development of the uncertainty over time or the inter‐temporal correlation of forecast errors for different horizons. This information is very important for forecast end‐users optimizing time‐dependent variables or dealing with multi‐period decision‐making problems, such as the management and operation of power systems with a high penetration of renewable generation. This paper provides input to these problems by proposing a model based on stochastic differential equations that allows generating predictive densities as well as scenarios for wind power. We build upon a probabilistic model for wind speed and introduce a dynamic power curve. The model thus decomposes the dynamics of wind power prediction errors into wind speed forecast errors and errors related to the conversion from wind speed to wind power. We test the proposed model on an out‐of‐sample period of 1year for a wind farm with a rated capacity of 21MW. The model outperforms simple as well as advanced benchmarks on horizons ranging from 1 to 24h. Copyright © 2016 John Wiley & Sons, Ltd.     

A compressed air energy storage system for wind power generation in western Inner Mongolia

内蒙古自治区陆上风能资源总储量达到了1380 GW,其中技术可开发量为380 GW,几乎占全国风电资源的一半.风力发电负荷已经占到内蒙古电网负荷的20%以上.针对这种情况,对大规模风电并入内蒙古电网进行了研究.在此研究基础之上,为了更有效的利用风电资源,设计了一台压缩空气储能电站.该压缩空气储能电站可以利用风电场夜晚的弃风电量进行储热,为白天的运行提供部分热源.最后,从热耗率,充电比与发电效率三方面对压缩空气储能电站进行了分析,研究结果表明,更高的储气压力和更大的储气容量能够得到更好的发电效率.     

A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe

Studies have shown that a large geographic spread of installed capacity can reduce wind power variability and smooth production. This could be achieved by using electricity interconnections and storage systems. However, interconnections and storage are not totally flexible, so it is essential to understand the wind power correlation in order to address power system constraints in systems with large and growing wind power penetrations. In this study, the spatial and temporal correlation of wind power generation across several European Union countries was examined to understand how wind ‘travels’ across Europe. Three years of historical hourly wind power generation data from 10 countries were analysed. The results of the analysis were then compared with two other studies focused on the Nordic region and the USA. The findings show that similar general correlation characteristics do exist between European country pairs. This is of particular importance when planning and operating interconnector flows, storage optimization and cross‐border power trading. Copyright © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd